In-Flight Testing & Verification of an Adaptive Distributed Fault-Tolerant Control Architecture

<p>The interest in utilizing multi-agent systems (MAS) has increased in the aerospace industry. Its scalability, efficiency, robustness, fault tolerance, and cost-effectiveness make it ideal for performing real-world missions that require more than one agent. However, in completing the tasks, the multi-agent systems are still vulnerable to environmental disturbances, cyber-attacks, and hardware failures. Therefore, an adaptive distributed fault-tolerant control architecture is needed to minimize the impacts of the previously stated circumstances and ensure the mission can continue successfully.</p> <p>This thesis describes the development of an experimental setup for testing and validating an adaptive consensus algorithm and a bio-inspired health management architecture. The distributed adaptive consensus algorithm gathers information states from the agents, processes the shared information, and decides the subsequent action for each agent until they all agree on a particular state. This act is also known as consensus. The adaptation law in the algorithm mitigates the effects of disturbances on the agents when trying to reach a consensus by minimizing the error between the system states and the reference model state. The addition of the health management system in the architecture is for detecting abnormal conditions, such as hardware failures and disturbances, alerting the other agents, and starting a joint action that mitigates the adverse effects. Inspired by the biological immune system, it mimics the capability of the human immune system in classifying threats as non-self and normal conditions as self. A supervised machine learning method, called Support Vector Machine, is utilized to improve its classification capabilities.</p> <p>These control architectures are implemented and flight tested on the developed testbed to analyze and verify that the intelligent architecture can drive the agents to reach a consensus and that the health management system can detect failures and mitigate their effects. The testbed consists of a multi-agent system of four Crazyflie 2.1 quadcopters, a VICON motion capture system for tracking the drones, and the Crazyswarm software architecture to send commands to the Crazyflie and receive data from the VICON system. In this thesis, a simulation environment incorporating the distributed control architecture and the nonlinear models of the quadcopters is also developed to perform preliminary tests and demonstrate that the desired results are obtainable.</p>

Name thesis:degree_name

Master of Aerospace Engineering

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Aerospace Engineering

Year dc:date.available

2023

Author dc:creator

• Budihartono, Michael

• Flight Test
• Crazyflie
• Multi-Agent System
• Consensus Algorithm
• Health Management System
• Distributed Control
• AIS-Inspired Adaptive Control
• Testbed
• Multi-Vehicle Systems and Air Traffic Control
• Navigation, Guidance, Control and Dynamics

Repository record dc:identifier

https://commons.erau.edu/edt/753

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1783